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CHAPTER 4
DEVELOPMENT OF PAVEMENT DETERIORATION
PREDICTION MODELS
4.1 GENERAL
An important feature of a pavement management system is its ability to
determine the current condition and to predict the future condition of the pavement
network. Pavement deterioration models relate functions which are measures of
distress to their causative factors. The principles involved in the development of
models include the selection of their mathematical form, role of statistics, and ability to
represent effects of different parameters. Deterioration models should be able to
predict the change in pavement condition, over a given period of time, under a set of
conditions. Deterioration of pavement is usually exponential in nature and its rate
varies as the condition of road varies with passage of time.
Pavement Condition Index (PCI) is a numerical index developed by US Corps
of Engineers (Shahin, 1994) intended to take care of numerous possible conditions of
pavement having various types, severity and density of distresses. PCI ranges from 0
for a failed pavement to 100 for a pavement in perfect condition. PCI provides an
index of pavement structural integrity and surface operational condition. Presently
routine maintenance priority for rural roads is assigned based on the pel value of roads
in each district subject to the availability of funds (Report of Working Group on Rural
Road, 11 th Five Year Plan, 2006).
Present study mainly aims at developing a complete Pavement Maintenance
and Management System (PMMS) for rural roads. Pavement deterioration modelling is
an essential component of a PMMS and hence an attempt has been made to model the
75
deterioration mechanism of rural roads and thereby to develop a suitable maintenance
strategy for rural road network. Distress prediction models were developed for various
types of distresses noticed on the road stretches. Prediction models were also
developed to predict the PCI and the progression of defection of rural roads. Earlier
deterioration models developed for rural roads follow the model form as that of major
roads incorporating traffic as the major parameter affecting pavement performance.
But for rural roads, traffic volume and axle loads are very low and consequently
structural distresses like rutting and cracking are almost absent.
4.2 METHODOLOGY
It is well known that the flexible pavements deteriorate over a period of time
due to the continuous movement of vehicles and exposure of pavements to
environmental factors. However the pavements are not constructed with proper quality
control in India. Based on the studies reported earlier, it has been found that one of the
major parameters contributing to the deterioration of pavements is Construction Quality
(CQ). Hence firstly an attempt was made to model construction quality of roads by
identifying the factors affecting the same and the exact CQ is calculated as a value
between zero and one. Deterioration models were developed for rural roads by
incorporating construction quality also as a parameter along with other causative
factors. Pavement performance prediction models were developed by using
conventional regression technique and also by using Artificial Neural Network (ANN)
technique.
Structural and functional condition data were collected from the road stretches
selected for the study as mentioned in Sections 3.3 and 3.4 for a period of three years.
In the detailed functional data collected, distresses were measured for varying levels of
intensity and expressed as percentage area of the carriageway affected.
76
various distresses, but also their severity levels have got different quantum of
contribution to the deterioration of pavement. For example, pothole of low severity
will have more effect on the deterioration of the pavement compared to ravelling of low
intensity. Hence in order to bring varying levels of intensity of each type of distress to
a single value of that particular distress, and also to convert each type of distress to a
common standard, weightages are to be assigned for each intensity level of each type of
distress. In this study, severity levels of each type of distress were assigned a
weightage as shown in Table 4.1 based on expert opinion.
Table 4.1 Weightages Assigned for Different Levels of Severity of Distresses
Distress Weightage Assigned
Low Medium High
Ravelling (%) 10 20 30
Edge Break (%) 20 30 30
Edge Drop (%) 20 30 40
Pothole (%) 50 60 70
It can be observed from Table 4.1 that pothole of low severity will have five
times effect as that of low ravelling on the total deterioration of pavement. The total
percentage of each distress was found out using these weightages in the following
manner. Let AI, A2 and A3 be the areas of low, medium and high ravelling, and then
the percentage area of ravelling is obtained as [(l 0*A 1+20*A2+30*A3)/(l 0+20+30)].
Similarly percentage area of each type of distress was calculated corresponding to each
set of condition data collected. The deflection data collected were converted in terms
of characteristic deflection as mentioned in Section 3.3. The statistics of functional and
structural condition data thus calculated are given in Table 4.2.
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Table 4.2 Statistics of Functional and Structural Condition Data
Parameter Min Max MeanStandardDeviation
Ravelling (%) 0.24 64.23 14.22 8.47
Pothole area (%) 0.00 2.64 0.84 0.62
Edge Failure (%) 0.49 46.80 8.66 9.65
Roughness (m/km) 6.05 11.09 8.60 1.20
MSN 1.52 2.60 2.06 0.33
Cumulative Standard Axles 0.01 0.978 0.342 0.287(CSA in msa)Characteristic Deflection (mm) 0.88 2.91 1.47 0.27
Construction Quality of the road sections was assigned values ranging from
zero to one depending on the value of carriageway camber, shoulder camber,
percentage reduction from the design thickness and relative compaction. These
parameters were assigned a range of values varying from the most desirable to least
desirable limits and each range was assigned a weight in proportion to its adequacy.
For example, maximum weightage of five was assigned for the most desirable range of
each parameter and the minimum of one was assigned for the least desirable range.
The weightages assigned for each range of parameters for determining construction
quality are shown in Table 4.3. Based on the values of the four parameters mentioned
above for each of the road stretches, due weightages were assigned and the total
weightage for each section was arrived at. If the total weight was twenty, then the CQ
was assigned a value of one, and reduced proportionately to zero when the total weight
decreases to the minimum value of four.
78
Table 4.3 Range of Values of Paramcters for Dctermining Construction Quality
Shoulder Carriageway% reduction in
Relative ConstructionDesign
Camber CamberThickness
Compaction Total Quality
Weight Weight Weight WeightWeight (CQ)
Range(WI)
Range(W2)
Range(W3)
Range(W4)
>3 5 >2.5 5 0-10 5 97-100 5 20 1
2.5-2.9 4 2-2.4 4 10-20 4 93-96 4 16 0.75
2-2.4 3 1.5-1.9 3 20-30 3 89-92 3 12 0.5
1.5-1.9 2 1-1.4 2 30-40 2 85-88 2 8 0.25
<1.5 1 <1.0 1 >40 1 <85 1 4 0
Construction quality assigned for the road stretches based on the parameters
considered as mentioned above are shown in Table 4.4.
Table 4.4 Construction Quality of Road Stretches Selected for the Study
Road Stretch Name of the road Construction
ID Quality
1 Ambalappuzha-Koopparakadavu 0.625
2 Vellaniilithodu - T S Canal 0.750
3 Thumboly East 0.750
4 Velloor church - Canal Bund 0.750
5 Muslim Church 0.625
6 Airapuram-Kuttipilly 0.625
7 Moscow - Meenadam Road 0.750
8 Mullaikkunnu-Kannuvetty Kannuvetty 0.5625
9 Vennimala-Nongal 0.5625
10 Velianoor 2 nd mile 0.750
11 Attachakkal East 0.625
12 Ebenezer- Thondada 0.625
13 Vavarambalam 0.750
14 Santhipuram 0.5625
15 Pulimath 0.625
79
4.3 DEVELOPMENT OF PAVEMENT DETERIORATION PREDICTIONMODELS BY REGRESSION
Pavement deterioration models express the change in distress over a time base
'1' and should be able to predict the change in pavement condition over a given period
of time under a set of conditions. Road deterioration is computed as the incremental
change in pavement condition over this time base, due to effects of pavement
characteristics, environmental factors and maintenance inputs. The principles involved
in the development of models include the selection of their mathematical form, the role
of statistics, and the ability to represent the effects of various parameters. Present
pavement condition is updated for each analysis period taking into account the
maintenance actions done on the road. Regression models in the present study were
developed using SPSS 14.0 (Statistical Package for Social Sciences). A model
represented in incremental form can take care of pavements in any initial stage of
condition and at any age and is represented in the form as:
Change in condition = function of (current condition of pavement, strength ofpavement layers, pavement age, environmental factors,incremental traffic, maintenance inputs)
As mentioned in Section 3.4, the distresses noticed on the study sections were
mainly functional like ravelling, pothole and edge breaking. Since no load associated
distresses were observed on the study sections, deflection measurements were taken so
as to have a record of the structural adequacy of pavement and its deterioration. Hence
prediction models were developed for predicting the following parameters:
• Construction Quality
• Ravelling Initiation,
• Ravelling Progression
• Pothole Progression
80
• Edge Failure Progression
• Roughness Progression
• Deflection Progression
• Prediction of PCI.
Step wise regression was adopted and traffic was proved to be an insignificant
parameter in the process. The deterioration of pavement is exponential in nature and
the rate of deterioration varies depending upon its condition with passage of time (Sood
et al.1996). As already known from many research studies that the pavement
deterioration is a nonlinear phenomenon, nonlinear models were developed in the
present study to express the deterioration mechanism of rural roads. Out of the total six
sets of pavement condition and roughness data and three sets of deflection data
collected, first five sets of condition data and two sets of deflection data were used for
development of deterioration prediction models and last set of data was used for the
validation of these models.
4.3.1 Construction Quality Model
Thorough investigation of the deterioration process of rural roads revealed that
the major parameter contributing to their deterioration is not traffic as in the case of
major roads. The traffic and the axle loads on low volume roads are quite low, and
there are no load associated distresses, but still the functional distresses are
incrementing at a fairly high rate. Extensive examination of the field condition of the
study stretches identified the reason for deterioration as lack of proper drainage and
inferior construction practices. Hence an effort was taken as mentioned in Section 4.2
to bring out the effect of construction quality in the deterioration of rural roads.
The adequacy of drainage of a pavement is much dependent on the camber of its
carriageway and shoulder and hence these terms are directly included in the modelling
81
procedure. Based on the procedure mentioned in the Section 4.2, the construction
quality values corresponding to various set of parameters were found out and used for
modelling purpose. Hence construction quality was modelled as a function of shoulder
camber, carriageway camber, relative compaction and percentage reduction in the
actual thickness provided in the field from the design thickness. The model form was
selected after repeated trial and error process and the final model developed is given
below:
CQ =- 7.14 + (CWCam) 0.078 + (ShCam) 0.036 + (RC) 0.359 + (Pthk) - 0.166 (4.1)
[n= 25, R2 = 0.86, SE = 1.32]
where,
CQ
ShCarn
CWCam
RC
Pthk
= Construction Quality
= Shoulder Camber (%)
= Carriageway Camber (%)
= Relative Compaction (%)
= Percentage Reduction from Design Thickness
Terms in the bracket represents the statistical parameters viz., n is the Number of
observations, R2 the Coefficient of determination and SE is the Standard Error
respectively.
4.3.2 Ravelling Initiation Model
Ravelling is a distress which falls under the broad mode of distress namely
disintegration which confines to surface course only. Ravelling is the weathering away
of the pavement surface caused by the dislodging of aggregate particle and loss of
bitumen binder. Ravelling initiation age is characterised as the pavement age at the
time of development of ravelling upto 2% of the surface area. Since ravelling is
confined to surface course only, it is modelled as a function of construction quality and
drainage rating (as given in Table 3.3) and the model is given in Eqn. 4.2.
82
AgeRVln = - 0.41 + (CQ) 0.69 + 0.5 (DR) 1.29
where,
(4.2)
[n= 28, R2 =0.7, SE= 0.987]
AgeRVln
CQ
DR
= Age of pavement in years at the time of ravelling initiation
= Construction Quality
= Drainage Rating
(4.3)
4.3.3 Ravelling Progression Model
Ravelling occurs due to loss of fines or stone particles from surface course
and/or loss of adhesion/bonding between binder and aggregate. Like other modes of
distress, once started, ravelling increase in extent and severity, and leads to
disintegration of the surfacing. It affects both structural and functional performance of
the pavement. Ravelling when developed beyond a certain extent leads to potholing.
The rate of ravelling progression is calculated by dividing the change in ravelled area
between two consecutive observations by the time difference. Initially trial attempts
were made to model ravelling progression as a function of construction quality, initial
raveling and pavement age and traffic in million standard axles (msa). But in the
stepwise regression process traffic proved to be an insignificant parameter and hence
omitted. The model form with the best statistical parameters was selected after a
number of repeated trials and is given below:
RVt/t = (RVixpage)0.553 + Page 0.637 + CQ- 2.168
[n=174, R2 = 0.67, SE=0.327]
where,
RVt
RVt/t
t
= Ravelling (%) after a time interval 't'
= Rate of raveling progression over a time interval '1'
= Time interval (years)
83
RY.1
CQ
Page
= Initial Ravelling (%) at the beginning of time interval or
= Construction Quality
= Age of pavement in years after time interval 't'
4.3.4 Pothole Progression Model
Potholes are the cavities on road surface and are generally bowl shaped.
Potholes are the most severe form of pavement distress, which is caused due to spalling
of wide cracks and disintegration of surfacing and subsequently the base material.
Potholes not only cause discomfort to the drivers but also effects loss of strength of
pavement. The road user cost is considerably high due to the presence of pothole on
account of damage caused to vehicle parts and reduction in travelling speed.
The factors responsible for pothole development are highly dependent on material
properties, quality of construction, thickness of bituminous layer, and traffic volume.
Strength of pavement is dependent to a great extent on the material properties and is
usually expressed by its Modified Structural Number (MSN). Moreover potholes
developed over a specific time period will be affected much by the initial potholes and
ravelling at the start of that time period. Pothole progression was thus modelled as
function of Modified Structural Number, pavement age since last renewal, initial
pothole, initial ravelling and construction quality. Traffic volume was excluded during
the modelling procedure since it proved to be insignificant. Models developed to
explain pothole progression is given below:
PHtit = 1.596+PHiO.937 +RVi
0.398 - 2.086(MSNxCQ)+ (ThbmxPage)0.097
(4.4)
[n=117, R2 = 0.513, SE=0.635]
where,
PHtit = Rate of pothole progression over a time interval 't'
84
PH.I
RV.I
CQ
MSN
Page
Thbm
= Initial Pothole Area (%) at the beginning of time interval '1'
= Initial Ravelling (%) at the beginning of time interval '1'
= Construction Quality
= Modified Structural Number
= Pavement Age in years at the end of time interval 't'
= Thickness of bituminous layer in mm
4.3.5 Edge Failure Progression Model
Edge breaking, another distress noticed on the study sections can be due to
movement of traffic along edges of pavement, edge drop between pavement edge and
shoulder and improper construction practices. Since traffic and axle loads are low,
effect of traffic was found to be negligible. Hence the edge breaking was modelled as a
function of initial edge break, edge drop, age of pavement and construction quality.
Eft = (EFixt)0.870 + (Ed
ixPage)0.2l3+ CQ- 3.04 (4.5)
[n=79, R2 = 0.816, SE=0.18]
where,
Ed.1
, Page
CQ
= Edge break (%) after a time interval 't'
= Initial Edge break (%) at the beginning of time interval '1'
= Initial Edge Drop (%) at the beginning of time interval '1'
= Age of pavement in years at the end of time interval '1'
= Construction Quality
4.3.6 Roughness Progression Model
Roughness is the undulation in the road profile and is of major concern to the
road users in their comfort perspective. The rate of distortion is accelerated, on
weakening of the pavement due to surface defects like cracking, ravelling and
potholing. Roughness affects the dynamics of moving vehicles, vehicle's wear and tear
85
and therefore has an appreciable influence on vehicle operating costs. It also imposes
dynamic loading on the road surface, thus accelerating the deterioration process.
Roughness progression is dependent on other surface distresses like ravelling,
pothole and strength of pavement. Roughness progression in terms of roughness at any
instant of time was modelled as function of Modified Structural Number, initial
roughness, initial ravelling, initial pothole area, pavement age since last renewal and
construction quality.
RGt
=4.27 + RViO.075 + (RGixt)0.338 + MSN-O.6812 + (PHixPage) 0. 116 (4.6)
where,
RGt
PHi
RGi
RV i
MSN
Page
[n=43, R2 = 0.504, SE =2.14]
= Roughness in IRI (m/km) after a time interval 't'
= Initial Pothole Area (%) at the beginning of time interval 't'
= Initial Roughness in IRI (m/km) at the beginning of time interval '1'
= Initial Ravelling area (%) at the beginning of time interval '1'
= Modified Structural Number
= Age of Pavement in years at the end of time interval '1'
4.3.7 Deflection Growth Models
Pavement deflection data was measured using a Benkelman Beam as explained
in Section 3.3. Characteristic deflection values were calculated using Eqn. 3.3 and the
increase of characteristic defection calculated for the road stretches with age are shown
in Fig. 4.1. No pavement can be constructed with zero deflection and hence there will
be an initial deflection even at the beginning of life of pavement and the deflection at
any later stage is very much dependent on this initial deflection value.
86
The characteristic deflection which is a token of the structural strength of
pavement thus is much affected by the initial deflection, strength of pavement, traffic
carried and the age of pavement at any time during the life.
O+--------,.-------,.-----------l
3.5 -,---------------------,.....E 3 +----------------------;§.
~ 2.5 +----------------------1~ ~"~$. 2 +----------'; -..------ .Ac _--- ~~ 1.5 ..--.... ,~- ~~'A _ _" --.:--
~ 1 +-__~~~ __==_-===::~Z-!:_ _()III
l; 0.5 +-----------------------1.s::.o
4.92 6
Age (years)
7
___ Stretch 1
Stretch 2
-+- Stretch 3
Stretch 4
-;I<- Stretch5
-.-Stretch 6
-Stretch 7
Stretch 8
stretch 9
Stretch 10
Stretch 11
Stretch 12
Stretch 13
Stretch 14
Stretch 15
Fig. 4.1 Variation of Characteristic Deflection of Road Stretches with Age
Characteristic deflection was modelled as a function of initial deflection, traffic
in terms of Cumulative Standard Axles in msa, Modified Structural Number to account
strength of pavement and age of pavement as shown below.
DefDef =Def. + 0.355(CSAxPage) i + MSN-1.472
t 1(4.7)
[n = 30, R2 = 0735, SE = 0.289]
where,
Def = Characteristic Deflection (mm) after a time interval '1't
Def. = Initial characteristic deflection (mm) at the beginning of time1
interval '1'
CSA = Cumulative Standard Axles in million
MSN = Modified Structural Number
Page = Age ofPavement in years at the end of time interval 't'
87
4.4 DEVELOPMENT OF DETERIORATION MODELS USING ARTIFICIALNEURAL NETWORK
Deterioration modelling of rural roads was also attempted usmg the soft
computing technique viz., Artificial Neural Network (ANN). The Graphical User
Interface (GUI) is a tool used to work with the neural network which facilitates to
create network, enter the data, train and simulate the networks. It is designed to be
simple and user friendly. Deterioration modelling using artificial neural network in the
present study was done using the Neural Network tool box available in
MATLAB 7.0.1.
Models were developed using Artificial Neural Network for
• Construction quality
• Ravelling Initiation
• Ravelling Progression
• Pothole Progression
• Roughness Progression
• Edge failure Progression
Modelling of deflection progression was not attempted using ANN, due to
the limited data size.
Modelling was done using the same parameters as those were used for the
deterioration modeling process using regression technique. The training parameters
include number of epochs and goal. Number of epochs was set to 100 and epoch goal
set to 0 for all models. The training was continued by changing type of transfer
function, the number of hidden layers, number of neurons in the hidden layer until the
error plot converges as close as possible to the goal value of zero. The network was
trained using 75% of available data and was simulated using remaining 25% of data
and the performance of the network was ascertained based on the target values.
88
4.4.1 Construction Quality Model
Construction quality data which was used for the regression modelling was
used for ANN modelling also. As mentioned in Section 4.3.1, the input vector includes
four parameters, viz., carriageway camber, shoulder camber, relative compaction of
subgrade layer and percentage reduction in the pavement thickness that is provided
from the design thickness. The theory behind formulation of an ANN model was
explained in Section 2.4.2.1. Repeated trials were done varying the transfer function,
the number of hidden layers, and number of neurons in the hidden layer till the training
error is the nearest possible to the goal. The architecture of the construction quality
model is shown in the Fig. 4.2. The first block shown in black colour is the input
vector and the number of input parameters is written below that block. Three layers
were then selected for the ANN model for construction quality of which two are hidden
layers and the last layer is the output layer. The hidden layers which are represented as
the second and third layers in Fig. 4.2 were selected with three neurons each and the
number of neurons is shown below each layer. The last layer is the output layer with
the number of output as the number of neurons shown below that layer.
Inputvector
4
HiddenLayerNo.1
3
HiddenLayerNo.2
3
Outputlayer
Fig. 4.2 ANN Architecture for Construction Quality Model
where r is the notation for tan-sigmoid transfer function in a layer.
89
Both the hidden layers and the output layer were selected with tan-sigmoid
transfer function as seen from Fig. 4.2. The network was trained with feed forward
back propagation algorithm. Simulation of the network was done with the remaining
25% data which was not used for training. The performance of the network was
expressed in terms of Mean Square Error (MSE) which reached a value of 0.001969.
The performance plot is shown in Fig. 4.3 and it is seen that the error starts
converging at 20 epochs.
Perforrrianceis 0.00196944, Goalis 010' r---""""T"""--.---~"-';"-.---~--..---~---r---'----:I
1001o':!> ~_-1-__~_--'-__~_--'-__.L...-_--'-__-'---_---'-_----J
o 10 20 30 40 50 60 70 80 90100 Epochsstop Training
Fig. 4.3 Training Error Convergence Plot for Construction Quality Model
4.4.2 Ravelling Initiation Model
Ravelling initiation observed on some of the roads during the initial phases of
data collection was used to model the ravelling initiation. The neural network model
for ravelling initiation can be expressed as:
Age of the pavement at the time of ravelling initiation = function of (CQ, Drainage)
90
The parameters of the input vector are drainage and construction quality.
The architecture consists of two layers of which one is a hidden layer and the
other one is the output layer. Hidden layer consists of three neurons and the
output layer has one neuron. Both hidden layer and output layer were trained with
tan- sigmoid transfer function and the network was trained with feed forward back
propagation algorithm. The architecture of the model is shown in Fig. 4.4.
Fig. 4.4 ANN Architecture for Ravelling Initiation Model
From the error convergence plot it was observed that the error starts
converging even after 40 epochs and the goal achieved was a Mean square Error
(MSE) of 0.007712.
4.4.3 Ravelling Progression Model
Parameters affecting progression of ravelling were identified as amount of
initial ravelling, age of pavement and construction quality. Hence the input vectors for
the ANN model for ravelling progression also include these three factors. Three layers
were selected of which two are hidden layers and the last one is an output layer.
The number of neurons in each hidden layer was fixed as five and the output layer
was selected with one neuron. The hidden layers were selected with tan-sigmoid
transfer function and the outer layer was selected with a purelin transfer function.
The Network architecture is shown in the Fig. 4.5.
91
t---l''"{'.,,~,w~,,~,WP')~
I I b{l} rlJ I b{2} ~rr-I b{3}~3 5 5 1
Fig. 4.5 ANN Architecture for Ravelling Progression Model
where ~ is the notation for purelin transfer function in a layer.
The network was trained with feed forward back propagation algorithm.
The MSE value obtained was 0.000953 and the error plot started converging at
around 20 epochs.
4.4.4 Pothole Progression Model
As mentioned in Section 4.3.3, the factors responsible for pothole
development are dependent on the material properties, drainage and construction
quality. Hence input vector consists of six parameters which include initial area
of potholes and ravelling, thickness of bituminous layer, Modified Structural
Number, age of pavement and Construction Quality. The architecture consists of
two layers, one hidden layer and one output layer. The hidden layer and the
output layer were selected with six neurons and one neuron respectively.
The hidden layer was selected with tan-sigmoid transfer function and the outer
layer was selected with a purelin transfer function. The network was trained with
feed forward back propagation algorithm and the architecture of the model is
shown in Fig. 4.6.
~ON{t.tl~ JFLWP.,}~
I I b{1} ru I b{2}~6 6 1
Fig. 4.6 ANN Architecture for Pothole Progression Model
92
From the error plot obtained it was observed that the MSE value started
converging after 80 epochs and reached a value of 0.019753.
4.4.5 Edge Failure Progression Model
The edge failure was another distress noticed on rural roads. The edge
failure at any point of time was modelled as a function of initial edge failure,
initial edge drop, age of pavement, time interval and construction quality and
hence the input vector consists of five elements. The network was trained with
feed forward back propagation algorithm. There are two hidden layers, the first
layer consists of 15 neurons and trained with tan-sigmoid transfer function and the
second hidden layer consists of nine neurons with purelin transfer function.
The output layer was selected with purelin transfer function. The architecture of
the network is shown in Fig. 4.7.
5 15 Q
Fig. 4.7 ANN Architecture for Edge Failure Model
It was observed from the error plot that the error converged to a value of 0.021501 after
90 epochs.
4.4.6 Roughness Progression Model
Input vectors for roughness progression model include initial roughness,
ravelled area, pothole area, MSN of pavement, age of pavement, construction
quality and the time interval for which roughness progression is estimated.
The ANN network obtained for roughness progression is shown in the Fig. 4.8.
93
7 12 1
Fig. 4.8 ANN Architecture for Roughness Progression Model
The input vector was selected with seven elements, the hidden layer with
12 neurons and the output layer with one neuron. The hidden layer was selected
with tan-sigmoid transfer function and the output layer was selected as a purelin
transfer function. The network was trained with feed forward back propagation
algorithm. It was observed from the error plot that the error converged to a value
of 0.00077596 at 60 epochs.
4.5 VALIDATION AND COMPARISON OF REGRESSION AND ANNDETERIORATION MODELS
4.5.1 Validation of Deterioration Models
Six sets of pavement condition and roughness data and three sets of rebound
deflection data were collected for the present study. The deterioration models developed
were validated using last set of data which was not used in the development of model.
The accuracy of models was checked using Chi-squared test and the results of validation
are shown in Table 4.5. The Chi-squared value observed for both regression and ANN
models were less than the Chi-Squared critical values at 5% level of significance.
Hence the models can be considered to be reliable at 5% level of significance based on
the Chi-squared test results. Further, it was also observed that the Chi-squared values
obtained for ANN models are less than that for regression models and it can be inferred
that the ANN models yield results closer to the observed values of distresses.
94
Table 4.5 Validation of Deterioration Models
Chi-squared Value
Degree ofChi-squared
(Observed)Deterioration Models Value
Freedom Regression ANN( Critical)Models Models
Construction Quality 16 26.3 1.69 1.020
Ravelling Initiation 25 37.65 5.25 1.239
Ravelling Progression 43 59.28 33.67 11.626
Pothole Progression 25 37.65 5.96 3.495
Roughness Progression 14 23.68 1.19 0.884
Edge Failure Progression 23 35.17 16.91 4.54
Deflection Progression 14 23.68 2.54 -
4.5.2 Comparison of Regression and ANN Deterioration Models
As mentioned in Section 4.5.1, validation of deterioration models developed
was done using last set of data which was not used for development of models. These
actual values of distresses observed on the roads which were used for validation were
plotted against both regression model predicted values and ANN model predicted values
so as to have a visual comparison among the results of the two modelling techniques.
The comparison of the distress values predicted by the two techniques along with actual
values of distress is shown in Figs. 4.9 to 4.13.
95
-'-Observed Values
_ Regression ModelPredicted Values
A Predicted Values
7.,-------------------.'E' 6 +----ii,..---------;.-COl~
C 5 +----+,...it------~S~ 4 ++-+Jr+-f--i+-I--'':::I
:.; 3 ~"""'--~------!~-""'-------lll2+_-------......----------l '-- ....J
~~1+_-----------------l~
~
<0-f-1......,-..,.--,-,.....,.--,-,......,.....,,...,.....,.--,......,-..,.--,-,.....,.--,-,-..,.--,......,--r-f
No.ofObsen'atioDs
Fig. 4.9 Variation between Observed and Predicted Age of RavellingInitiation
60.00 ~----------------.
50.00 +--------------=----i
40.00 +---_;-----------it-~.-.t--l30.oo +-'=:---f1-------i\------I-4-1&-l
~~
~ 20.00
10.00 ....----------------1
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43
No.ofObservations
-.-Measured Values
__ Regression ModelPredicted Vlues
ANN Predicted Values
Fig. 4.10 Variation between Measured and Predicted Ravelled area
96
3.50 .,------------------,
3.00
2.50.-.i-
2.00-~oS....
1.50=0..
1.00
-+-Measured alues
_ Regression ModelPredicted Values
-Ir-A PredictedValues
1 3 5 7 9 11 13 15 17 19 21 23 25
No.of obsenratlons
Fig.4.1l Variation between Measured and Predicted Potholed Area
14
12
10
=i- 8-~ 6=~
t:;.~ 4o.c~
r..:l2
o ,1 3 5 7 9 11 13 15 17 19 21 23
No.ofObsenratloDS
-+-Measured Values
- RegressionModel PredictedValuesA PredictedValues
Fig. 4.12 Variation between Measured and Predicted Edge Failure
97
14 r----------------,
12 +---------------=-1
•
10 +----+::::JIb:I!~-_I~-__JjIC--1
, 8
--6+--------------~e;4+--------------~
2+-----------------1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
No.ofObsen'atlons
-+-Measured Values
___ Regression Model
Predicted \ alues
ANN Predicted Values
Fig.4.13 Variation between Measured and Predicted Roughness
It can be observed from Figs. 4.9 to 4.13 that the ANN predicted values of
distresses are more close to the actual field data than regression predicted values.
The inherent advantage of Artificial Neural Network to deal with the uncertainty
associated with the pavement performance data over years is established in the
performance modeling of rural roads also. Hence ANN can be considered as a better
deterioration modelling technique for rural roads than regression method. ANN models
could not be developed for deflection progression hence the comparison between the
measured deflection values and deflection values predicted by regression method was
only done and is shown in Fig. 4. 14.
98
c
CJ).
PCI of
vano agen ies to represent
Inde (P nand
(PCI) is ch a composite
types of distresses, its severity
Pel nmge from 0 to 100 which
ex£le:lmld: CllJOOllb.Clo 0 pav respecti e . At present rural
00 PCI aI , hence an effort was
UIIh.....h will facilitate the future budget
m
m eiJdlutistriC1
aI score based upon the type,
~. nn,,"," ofpa: ement.
Pel is ~re2S'i::d by COIIIlubdti.ve dedlJrct
quanlJl1y 3111d se:werity -.,,-..... ofdJ-lSlre&s
99
PCI of the road sections were calculated by the deduct value method proposed
by Shahin (Shahin, 1994). As mentioned in Section 3.4, the distresses on the road
surface were recorded based on their severity levels. Total amount of each distress at
each severity level was found and expressed as a percentage density by dividing by the
total pavement area and multiplying by 100. The deduct value corresponding to each
distress at each severity level was found from the deduct value curves. If only one
deduct value is greater than two, the total deduct value was used instead of maximum
corrected deduct value. To calculate the maximum Corrected Deduct Value, the
individual deduct values were listed in the descending order, and the allowable number
of deducts, om' was calculated using the equation m= 1+ (9/98) (l00 - HDV i), where
HDVj is the highest individual deduct value for the ith road section. The number of
individual deduct values were limited to allowable number of deduct values, 'm', and
the sum of this individual deduct values was found out including the fractional part.
The number of deduct values, 'q' whose value is greater than two was also noted.
The Corrected Deduct Value (CDV) was determined from the correction curve
proposed by Shahin (Shahin, 1994) based on the 'q' value and total deduct value.
Then the value of the smallest individual value greater than two was reduced to two
initially and the CDV was determined and this process was repeated in subsequent
steps till the number of deduct values greater than two was reduced to one.
The maximum CDV is the greatest of the CDV values determined in this process and
PCI was calculated as (100 - I,(CDV)). Sequence of procedure for calculation of PCI
is shown in Fig. 4.15.
100
*1Define Type of Pavement .-.>. (
~ ~
1 ~6'.'hIi{,Define inspection method;Manual Visual Inspection
1Inspect selected representative rural road
section and identity its problem
JIdentify distress type and determine its
severity
~Measure density of each type of distress by
Amount of Distress [ ]Density = x 100 Expressed as%
Area of Sample Unit
~Obtain deduct point for each type of distress from deductvalue curve based on its severity level and density
~Calculate the sum of all deduct points
\
tObtain Corrected Deduct Value based on number of observed
distress and the summation of deduct points, provided indeduct curve
~Calculate PCI
PCI= 100- I Corrected Deduct Values (CDV)
Fig. 4.15 Procedure for Calculation of PCI (Shahin, 1994)
PCI of the entire road stretches were thus calculated by the method of deduct
values and its decrease with respect to age of pavement is shown in Fig. 4.16.
101
90
80
70
60
~ 50';
~ 40u:..30
20
10
03.67 4.67 5.08
AGE (Years)
5.92 6
-+-Stretcch No.1
_Stretch No.2
......-Stretch No.3
Stretch No.4
-+-Stretch NO.5
-.-S1retch No.6
-+-Stretch No.7
-Stretch nO.8
-Stretch No.9
Stretch No.10
Stretch No.11
Stretch No.12
Stretch No.13
Stretch NO.14
Stretch NO.15
Fig. 4.16 Variation of PCI with Age of Pavements
4.6.2 Development of PCI Prediction Model
PCI of the road stretches were found to deplete not only with age but also with
compromise in construction quality. Reduction in PCI was modelled as a function of
age and construction quality using regression technique and is shown below:
PClt
= PCIO
- 3.682 x (page)1.822 + e(O.55 x Page x CQ)
[n = 75, R2 = 0.835, SE = 1.44]
where,
(4.8)
PClt
PCIo
Page
CQ '
= PCI of pavement at the end ofa time interval 't'
= Initial PCI ofPavement at the beginning of time interval 't'
= Age ofPavement at the end of time interval time 't'
= Construction Quality
Effect of construction quality of a pavement on its PCI value over its age was
analysed by plotting the variation of PCI of road stretches with age for varying
construction quality values from 0 to 1 and is shown in Fig. 4.17.
102
100
90
80
70.....CQ=O
60_CQ=O.25
0 50A. CQ=0.5
40~CQ=0.75
30 CQ= 1.0
20
10
01 2 3 4 5 6 7
Age (Years)
Fig. 4.17 Variation of PCI with Age of Pavement for Varying ConstructionQuality
It can be seen from Fig. 4.17 that PCl value of road stretches do not vary much
with their construction quality upto an age of four years. Hence it can be inferred that
the influence of construction quality on the PCl value of a road stretch is prominent
beyond an age of four years.
Validation of the PCl model developed was done using the last se of data
which was not used for development of model. The accuracy of model was checked
using Chi-squared test. The Chi-squared value observed is 3.23 which is less than the
Chi-Squared critical value of 24.9 for a degree of freedom of 15 at 5% level of
significance and hence the model can be considered to be significant.
4.7 DISCUSSION
Pavement condition data, roughness data, deflection data and traffic volume
data were collected over a period of three years from the rural roads selected for the
study. Major distresses noticed on these roads were ravelling, potholes and edge
103
failure. Traffic volume and the axle loads on these roads were very low and hence no
load associated distresses like rutting and cracking were noticed on these roads.
Construction quality and provision of proper drainage were identified as two major
parameters affecting performance of rural roads. Unlike the PPS study, the
construction quality in the present study was quantified as a value between 0 and 1
based on the camber of carriageway and shoulder, relative compaction of subgrade and
percentage reduction in the pavement thickness actually provided. Prediction models
were developed for progression of ravelling, pothole, edge failure, roughness and
deflection using conventional regression technique and Artificial Neural Network.
The construction quality of roads selected for the study ranges between 0.5625
and 0.75. For predicting the distresses, CQ of 0.75 and 0.375 were taken as high and
low quality of construction respectively. For a road with high quality of construction
and good drainage rating, the ravelling initiation model predicts that ravelling would
start after 4 years and for road with poor construction quality and poor drainage
facilities ravelling would initiate after one month. Ravelling progression model
predicts that, for good quality roads ravelling will progress at a rate of 3.46 % and for
poor quality roads at a rate of 10.08 % after the first year. MSN value of the study
stretches ranges between 1.5 and 2.6 and a value of 2.1 is taken as the average value.
From the pothole progression model, it is seen that for an MSN value of 2.1, for good
quality roads the progression rate is 0.38% and for poor roads it is 2.17 % after the first
year. Edge Failure model predicts that edge failure reaches a value of 5.38% and
21.5% respectively for good quality and poor quality roads after the first year.
Roughness progression model predicts that roughness reaches a value of 6.4 m/km after
first year for a road with MSN value of 2.1. For prediction of deflection progression,
the initial deflection was arbitrarily assumed as 1.25 mm and the average CSA value
104
estimated from the traffic volume count i.,e., 0.342 msa was considered. Deflection
prediction model predicts that for a road with MSN value of 2.1, the characteristic
deflection reaches a value of 1.65 mm and 2.25 mm respectively at the end of first year
and fifth year respectively. For roads with an MSN value of 1.5, the deflection reaches
a value of 1.89 mm and 2.5 mm, and for roads with an MSN value of 2.6, the deflection
reaches a value of 1.58 mm and 2.1 mm respectively at the end of first year and fifth
year respectively.
Comparison of predicted distress value obtained by both Regression method
and Artificial Neural Network with the measured values from the field revealed that
ANN can be more effectively used as a modelling technique than regression technique
for the performance prediction modelling of rural roads.
Pavement condition prediction model was also developed for the prediction of
the composite index, PCl. It was observed that the effect of construction quality on the
PCI of. roads was considerable after an age of four years only. As per PCI prediction
model, for a road of good construction quality, PCI value decreases from 98 from the
end of the first year to 16 at the end of sixth year if no maintenance actions are done.
For a road of poor construction quality, PCI value decreases from 97.5 from the end of
the first year to 7.5 at the end of sixth year.
105
